Inducing multi-level institutional change through participatory plant breeding in southwest China
Bibliographic record
Abstract
The expansion of modern hybrid varieties in maize (Zea mays L.) in China has caused the rapid loss of local varieties, including farmer maintained varieties and landraces. With growing international recognition of the importance of agro-biodiversity conservation and on-farm crop improvement, participatory plant breeding has been accepted as a complementary strategy in modern agricultural research. Participatory plant breeding has been conducted in Guangxi province in the Southwest China since 2000. It has introduced technical options to the formal breeding programme as well as institutional options at a range of levels, including new, formalised benefit sharing arrangement among stakeholders. This study seeks to understand these challenges and, through action research to induce supportive multi-level institutional change through and for participatory plant breeding within the local socio-technical context. The core research questions of this study are: • What were the changes in the distribution of maize landraces and hybrids from 1998 to 2008 in the three southwest provinces, Guangxi, Yunnan and Guizhou? • What are the potentials of participatory plant breeding for maize hybrid improvement in this context? • How do the institutional changes in the organization of the seed supply system in China influence small holder-oriented seed supply? • What is the contribution of action researching to institutional innovation in the case of developing access and benefit sharing mechanisms in the context of the participatory plant breeding programme in southwest China? • How has public value been created, strengthened and rewarded through participatory plant breeding and related market mechanisms in selected cases?
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".